A novel method to form single-phase high entropy silicide materials by multilayer metal deposition and controlled heat treatment on silicon substrates.

Brief description not available

Researchers at the University of California, Davis have engineered dominant negative CD40L mutant polypeptides that inhibit CD40/CD40L-mediated signaling, offering therapeutic potential for inflammatory, immune disorders, and cancer with improved safety profiles.

Kainate receptors, also known as kainic acid receptors are ionotropic receptors that bind to and are responsive to glutamate in neurons. These were originally identified as being activated by the compund kainic acid, orignally isolated from algae. Postsynaptic kainate receptors are involved in excitatory neurotransmission while presynaptic kainate receptors are involved in inhibitory neurotransmission. Kainic acid is a potentially very useful compound but very difficult to synthesize. As a result, there are very few pharmacological tool compounds to study kainate receptors and none that are readily tunable to install labeling compounds. 

Optical neural networks (ONNs) are a promising computational alternative for deep learning due to their inherent massive parallelism for linear operations. However, the development of energy-efficient and highly parallel optical nonlinearities, a critical component in ONNs, remains an outstanding challenge. To address this situation, researchers at UC Berkeley and Berkeley National Lab developed a nonlinear optical microdevice array (NOMA) compatible with incoherent illumination by integrating the liquid crystal cell with silicon photodiodes at the single-pixel level. The researchers fabricated NOMA with over half a million pixels, each functioning as an optical analog of the rectified linear unit at ultralow switching energy down to 100 femtojoules/pixel. The team demonstrated an optical multilayer neural network. This work holds promise for large-scale and low-power deep ONNs, computer vision, and real-time optical image processing.

The technology, known as Speech Articulatory Coding (SPARC), is a neural encoding-decoding framework for speech. It works by inferring articulatory features from audio and then synthesizing new speech from those features. The system effectively disentangles the speaker's identity from the speech's articulation, enabling accent-preserving voice conversion and providing a foundation for real-time voice privacy protection.

This technology uses smartphones to monitor a building's structural health by recording ambient vibrations. The data is processed to determine structural parameters, establishing a baseline to identify changes that may indicate damage, and trigger inspections.

A verbatim phoneme recognition framework that transcribes what a person actually says, including accents and dysfluencies, to provide precise feedback for pronunciation training.